Entropy Minimization methods use a loss term which is applied on the unlabeled data to force the model \(f_{\theta }(x)\)  to produce confident predictions (i.e., low-entropy) for all of the samples, regardless of what the actual labels are [1]}. For example, by assuming the softmax layer of a CNN has \(c\)  outputs, the loss term applied on unlabeled data is as follows: \(-\sum _{i=1}^c f^{(i)}_{\theta }(x) \log f^{(i)}_{\theta }(x) \) . Ideally, this class of methods penalizes the decision boundary that passes near the data points, while they instead force the model to provide a high-confidence prediction [1]}. It has been shown that entropy minimization on its own, can not produce competitive results [3]}. However, entropy minimization can be used in conjunction with VAT (i.e., EntMin VAT) to provide state of the art
results in which VAT assumes a fixed virtual label prediction in the regularization \(d({f_{\theta }}(x), f_{\theta }(x+r))\)  [4]}.
